1. Field of the Invention
This invention generally relates to methods and systems for generating unbiased wafer defect samples.
2. Description of the Related Art
The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.
One of the most important tasks during setup of wafer inspection recipes is to identify as many defect types, both real and nuisance, as can be detected on a wafer. As automated recipe setup and tuning becomes more important, the need to automatically identify a good set of defects for this optimization (both nuisance and real) becomes increasingly important as well. Without a good training set, automated optimization cannot function well. In addition, during manufacturing ramp up, when high and unknown defectivity is an issue, it is equally important to identify all defects on a wafer, even though in this situation, the interest is primarily in killer defects.
The need for developing effective sampling algorithms that achieve maximum defect type diversity for both real and nuisance events has been growing with the increasing challenges in optical inspections. As the sizes of defects of interest (DOIs) shrink, optical inspections struggle to maintain differential sensitivity to these defects. To achieve the desired sensitivity, inspections tend to rely less on sophisticated defect detection algorithms and more on complex nuisance filters that leverage the wealth of defect properties (or attributes). However, tuning such filters requires a defect population that is representative of all defect types, both nuisance and real.
Examples of some methods that are currently used to sample defects from a population are described in U.S. Pat. No. 6,265,232 issued Jul. 24, 2001 to Simmons, U.S. Pat. No. 6,613,590 issued Sep. 2, 2003 to Simmons, U.S. Pat. No. 6,792,367 issued Sep. 14, 2004 to Hosoya et al., U.S. Pat. No. 6,890,775 issued May 10, 2005 to Simmons, and U.S. Pat. No. 7,912,276 issued on Mar. 22, 2011 to Shibuya et al. and U.S. Patent Application Publication Nos. 2005/0158887 published Jul. 21, 2005 to Simmons and 2008/0295048 published on Nov. 27, 2008 to Nehmadi et al., all of which are incorporated by reference as if fully set forth herein.
Four different methods are also available with products that are commercially available from KLA-Tencor, Milpitas, Calif. for sampling diverse populations of defects. For example, on-tool diversity sampling (DS) uses a mix of a hard-coded attribute-based binner and an unbiased diversification algorithm using the defect feature vector space. Initial Defect Finder (IDF) is available off-line in Impact software and combines the power of Smart Sampling with iDO binning and with the ability to accumulate a sample from diverse scans without double sampling into a single diverse sample. Class code sampling (CCS) is available both on-tool and off-line and achieves diversification through careful manual tuning of iDO classifiers and targeted sampling from the various bins. In addition, rule based sampling (RBS) is available in Klarity Defect and also on-tool under the name of Precision Sampling and works in principle in the same way as CCS.
On-tool DS definitely improves sample diversity when compared to random sampling. It does find occasional use in the field as an initial defect finder, but, by and large, it has not been strongly adopted. There are two reasons for the low adoption. First, the sample diversification is typically incomplete and defect types are routinely missed. In addition, there is no way to adjust the sampling behavior (except for sample size) or to modify the diversification criteria. Fundamentally, there are two problems with the diversification approach of this sampling scheme. First, it relies on a hard-coded binner, which is not adaptive to the data. Second, the feature vector space is substantially large (about 80 dimensions) with many correlated and noise features that make the diversification in the space inefficient.
CCS relies entirely on iDO binning as a diversification mechanism, which poses two fundamental problems for unbiased discovery. For example, by its nature, iDO binning requires construction and tuning of the classifier trees, a process that requires prior knowledge and some assumption about defect properties on the wafer. This is obviously difficult before all defects have been discovered. In addition, such trees are not adaptive to the data and cannot work well across the board even if they work well on one dataset. Any qualitatively new wafer needs new binner tuning to achieve best diversification. The second problem is that no diversification within bins is possible and thus this sampling only works well when the bins are fairly homogenous, which is hardly ever the case. RBS is plagued with the same problems as CCS. Even though it does not rely necessarily on the iDO binner, in effect, the sampling rules set up cut lines and thresholds just as iDO does.
IDF, as implemented today, relies on iDO binning for diversification and on CCS to produce the diverse sample and thus contains all the disadvantages of that sampling scheme. Its ability to (a) accumulate a sample across different scans without double sampling and (b) use capture rate of the defects in multiple scans as a new diversification attribute are its only advantages over CCS.
Accordingly, it would be advantageous to develop systems and/or methods for generating a defect sample for a wafer that can find a diverse population of real and nuisance defects in substantially noisy inspections, go beyond the capabilities of the current methods, are much simpler to use, significantly improve time to result, and retain the flexibility of configuring biased sampling schemes in cases that some prior knowledge exists.